Multi-scale study and modeling of coal-gangue-based backfill material with MgO expansion agent under low-temperature curing conditions
Source abstract
Abstract
Low-temperature curing severely compromises coal-gangue-based backfill (CGB) performance, posing safety risks in cold-region mining. This research evaluates the effect of MgO expansive agent (MEA) in enhancing strength and mitigating damage in CGB under 5 °C curing conditions. An integrated methodology that combines uniaxial compression testing (UCT) with acoustic emission (AE) monitoring and scanning electron microscopy (SEM) is employed. Results demonstrate that 6‰ MEA by binder mass is the optimal dosage, elevating the 28-day uniaxial compressive strength (UCS) and elastic modulus (E) to 2.83 ± 0.15 MPa and 300.55 ± 25.8 MPa, respectively. The enhancement mechanism involves a transition from brittle to ductile failure. AE analysis reveals that the 6‰ dosage alters the cracking mode, increasing tensile cracks from 68.9% to 83.2% while decreasing shear cracks. It delays catastrophic failure, as indicated by the shift in the AE b -value inflection, signaling imminent instability, from approximately 70% of peak stress in the control to nearly 90%. On the microscopic scale, this toughening effect results from the synergistic interaction of Mg(OH) 2 crystals and C-S-H gels that reduce interfacial transition zone (ITZ) defects and densify the matrix, as confirmed by quantitative image analysis. Based on the above physical understanding, a piecewise damage constitutive model was established in strain space, while AE observations were used to characterize the staged damage evolution and support the model’s physical interpretation. The model accurately predicts the complete stress-strain response, providing a robust tool for the design and safety assessment of durable backfills in cold regions.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Tailings Management and Properties · Concrete and Cement Materials Research · Materials Engineering and Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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